Corpus

TODO: check for grammar

Out with the old: start of a beatiful thing

After almost twelve years of studying music from the optics of a jazz musician, you could say that this has influenced my taste in music and musical analytic capacity by a significant amount. Playing in a bigband twice a week and simply being around a lot of jazz started to consume my being with regards to thinking, listening and of course playing. After a series of unfortunate events, the members of our bigband decided to part ways and around this time I made the most important decision to start paying for a Spotify account. This marks the start of a — heavily influenced by jazz — liked playlist.

Changes in life and music

Two years go by and the decision is made to go on exchange and have the time of my life. In this period where I was supposed to be studying, I learned to appreciate going out and clubbing with a specific love for minimal techno and deep house music. These styles of music are repetitive and not always the most complex, which wouldn’t have made my heart beat faster in the past. Even so, this new found appreciation for a new style of music must have changed the mean characteristics of a song present in my Spotify liked playlist by a lot.

Analyse what you like

Because I am aware of this shifting trend in my own music taste, I hope to see this reflected when looking at a deeper analysis of my Spotify liked playlist by using the Spotify API. Since an account’s liked playlist is the only playlist which can not be made public, I’ve had to use a workaround and export my liked playlist as a csv file through a service called Skiley. I could not copy and paste my liked songs to a regular Spotify playlist and export them that way, since the dateAdded feature wouldn’t be preserved that way, which is a most-important feature when performing an analysis over time. The resulting corpus exists of 1338 songs, accumulated over two and a half years of joyous listening.

    addedAt                       albumArtistsNames   albumName        
 Min.   :2021-10-21 10:34:26.00   Length:1338        Length:1338       
 1st Qu.:2022-07-29 01:20:55.75   Class :character   Class :character  
 Median :2023-01-21 06:50:30.50   Mode  :character   Mode  :character  
 Mean   :2022-12-29 14:43:02.48                                        
 3rd Qu.:2023-06-02 13:48:52.75                                        
 Max.   :2024-02-22 21:03:25.00                                        
 albumPopularity albumRecordLabel   albumReleaseDate     artistFollowers   
 Min.   : 0.00   Length:1338        Min.   :1962-06-30   Min.   :       6  
 1st Qu.:26.00   Class :character   1st Qu.:2008-01-06   1st Qu.:   23438  
 Median :43.00   Mode  :character   Median :2018-09-03   Median :   94142  
 Mean   :41.09                      Mean   :2012-06-26   Mean   : 1645946  
 3rd Qu.:57.00                      3rd Qu.:2021-07-29   3rd Qu.:  717599  
 Max.   :91.00                      Max.   :2024-01-12   Max.   :84097254  
  artistName        artistPopularity secondaryArtistsNames trackDuration    
 Length:1338        Min.   : 0.00    Length:1338           Length:1338      
 Class :character   1st Qu.:37.00    Class :character      Class1:hms       
 Mode  :character   Median :47.00    Mode  :character      Class2:difftime  
                    Mean   :48.84                          Mode  :numeric   
                    3rd Qu.:61.00                                           
                    Max.   :96.00                                           
 trackFeatureAcousticness trackFeatureDanceability trackFeatureEnergy
 Min.   :0.0000057        Min.   :0.0000           Min.   :0.0260    
 1st Qu.:0.0281000        1st Qu.:0.6350           1st Qu.:0.5272    
 Median :0.1140000        Median :0.7400           Median :0.6600    
 Mean   :0.2025144        Mean   :0.7131           Mean   :0.6509    
 3rd Qu.:0.3020000        3rd Qu.:0.8140           3rd Qu.:0.7917    
 Max.   :0.9920000        Max.   :0.9880           Max.   :0.9980    
 trackFeatureInstrumentalness trackFeatureKey  trackFeatureLiveness
 Min.   :0.0000000            Min.   : 0.000   Min.   :0.01730     
 1st Qu.:0.0000095            1st Qu.: 2.000   1st Qu.:0.08875     
 Median :0.0078600            Median : 6.000   Median :0.11750     
 Mean   :0.2432348            Mean   : 5.598   Mean   :0.18041     
 3rd Qu.:0.5465000            3rd Qu.: 9.000   3rd Qu.:0.21175     
 Max.   :0.9690000            Max.   :11.000   Max.   :0.95400     
 trackFeatureLoudness trackFeatureMode trackFeatureSpeechiness
 Min.   :-22.863      Min.   :0.000    Min.   :0.00000        
 1st Qu.: -9.930      1st Qu.:0.000    1st Qu.:0.04822        
 Median : -7.868      Median :1.000    Median :0.08050        
 Mean   : -8.276      Mean   :0.503    Mean   :0.13543        
 3rd Qu.: -6.257      3rd Qu.:1.000    3rd Qu.:0.20075        
 Max.   :  0.920      Max.   :1.000    Max.   :0.85600        
 trackFeatureTempo trackFeatureTimeSignature trackFeatureValence
 Min.   :  0.00    Min.   :0.000             Min.   :0.0000     
 1st Qu.: 94.12    1st Qu.:4.000             1st Qu.:0.4898     
 Median :118.00    Median :4.000             Median :0.6610     
 Mean   :118.15    Mean   :3.953             Mean   :0.6319     
 3rd Qu.:132.48    3rd Qu.:4.000             3rd Qu.:0.8090     
 Max.   :210.16    Max.   :5.000             Max.   :0.9890     
  trackName         trackPopularity  trackNumber     artistGenres      
 Length:1338        Min.   : 0.00   Min.   : 1.000   Length:1338       
 Class :character   1st Qu.:24.00   1st Qu.: 1.000   Class :character  
 Mode  :character   Median :41.00   Median : 3.000   Mode  :character  
                    Mean   :39.53   Mean   : 4.552                     
                    3rd Qu.:55.75   3rd Qu.: 7.000                     
                    Max.   :94.00   Max.   :24.000

Analysis

temporary sandbox

Lorem Ipsum

Chroma Features

TODO: write

Structure Analysis and Sef-similarity matrices

TODO: write

Key and Chord Estimation

TODO: write

Novelty Functions and Tempograms

TODO: write

Classification and Clustering

TODO: write

Did I succeed?

TODO: write

In short: NO

---
title: "new corpus compMusic"
output:
  flexdashboard::flex_dashboard:
    # orientation: columns
    storyboard: true
    social: menu
    source: embed
date: "2024-02-23"
---
```{r setup, include=FALSE}
# knitr::opts_chunk$set(echo = TRUE)
library(flexdashboard)
```

```{r, echo=FALSE}
library(tidyverse)
library(spotifyr)
library(ggplot2)

df <- read_csv(
  "Liked Songs.csv",
  show_col_types = FALSE
  ) %>% 
  subset(select = -c(
    isLocal,
    isLikedByUser,
    trackIsrc,
    trackUrl,
    artistUrl,
    albumUrl,
    albumUpc,
    albumType,
    addedBy
    )) %>%
  arrange(addedAt)

df_stats_global <- df %>%
  summarise(
    mean_speechiness = mean(trackFeatureSpeechiness),
    mean_acousticness = mean(trackFeatureAcousticness),
    mean_liveness = mean(trackFeatureLiveness),
    sd_speechiness = sd(trackFeatureSpeechiness),
    sd_acousticness = sd(trackFeatureAcousticness),
    sd_liveness = sd(trackFeatureLiveness),
    median_speechiness = median(trackFeatureSpeechiness),
    median_acousticness = median(trackFeatureAcousticness),
    median_liveness = median(trackFeatureLiveness),
    mad_speechiness = mad(trackFeatureSpeechiness),
    mad_acousticness = mad(trackFeatureAcousticness),
    mad_liveness = mad(trackFeatureLiveness)
  )
```

Corpus
=======================================================================
TODO: check for grammar

#### Out with the old: start of a beatiful thing
After almost twelve years of studying music from the optics of a jazz musician, you could say that this has influenced my taste in music and musical analytic capacity by a significant amount.
Playing in a bigband twice a week and simply being around a lot of jazz started to consume my being with regards to thinking, listening and of course playing.
After a series of unfortunate events, the members of our bigband decided to part ways and around this time I made the most important decision to start paying for a Spotify account.
This marks the start of a --- heavily influenced by jazz --- liked playlist.

#### Changes in life and music
Two years go by and the decision is made to go on exchange and have the time of my life.
In this period where I was supposed to be studying, I learned to appreciate going out and clubbing with a specific love for minimal techno and deep house music.
These styles of music are repetitive and not always the most complex, which wouldn't have made my heart beat faster in the past.
Even so, this new found appreciation for a new style of music must have changed the mean characteristics of a song present in my Spotify liked playlist by a lot.

#### Analyse what you like
Because I am aware of this shifting trend in my own music taste, I hope to see this reflected when looking at a deeper analysis of my Spotify liked playlist by using the Spotify API.
Since an account's liked playlist is the only playlist which can not be made public, I've had to use a workaround and export my liked playlist as a csv file through a service called *Skiley*.
I could not copy and paste my liked songs to a regular Spotify playlist and export them that way, since the *dateAdded* feature wouldn't be preserved that way, which is a most-important feature when performing an analysis over time.
The resulting corpus exists of 1338 songs, accumulated over two and a half years of joyous listening.


```{r, include=TRUE}
print(summary(df))
```


Analysis {.storyboard}
=======================================================================
### temporary sandbox
```{r, include=TRUE}
plt <- df |>
ggplot(
    aes(
      x = "dateAdded",
      y = "trackFeatureTempo",
      size = "trackFeatureEnergy",
      # colour = mode,
      # text = track.name
    )
  ) +
  geom_point()

plt
```

***
Lorem Ipsum


### Chroma Features
***
TODO: write

### Structure Analysis and Sef-similarity matrices
***
TODO: write

### Key and Chord Estimation
***
TODO: write

### Novelty Functions and Tempograms
***
TODO: write

### Classification and Clustering
***
TODO: write

Did I succeed?
=======================================================================
TODO: write

In short: NO